Dynamics of information diffusion and disease transmission in time-varying multiplex networks with asymmetric activity levels

Abstract

While the interaction between information and disease in static networks has been extensively investigated, many studies have ignored the characteristics of network evolution. In this study, we construct a new two-layer coupling model to explore the interactions between information and disease. The upper layer describes the diffusion of disease-related information, and the lower layer represents the disease transmission. We then use power-law distributions to examine the influence of asymmetric activity levels on dynamic propagation, revealing a mapping relationship characterizing the interconnected propagation of information and diseases among partial nodes within the network. Subsequently, we derive the disease outbreak threshold by using the microscopic Markov-chain approach (MMCA). Finally, we perform extensive Monte Carlo (MC) numerical simulations to verify the accuracy of our theoretical results. Our findings indicate that the activity levels of individuals in the disease transmission layer have a more significant influence on disease transmission compared with the individual activity levels in the information diffusion layer. Moreover, reducing the damping factor can delay disease outbreaks and suppress disease transmission, while improving individual quarantine measures can contribute positively to disease control. This study provides valuable insights into policymakers for developing outbreak prevention and control strategies.